Document Actions

The Center for Materials Research

Welcome to the web sites of the Center for Materials Research

Welcome to the web sites of the Center for Materials Research!

The Center for Materials Research is an interdisciplinary university research center at JLU.
It is dedicated to the implementation of joint research projects, the support of the study courses in materials science and it promotes scientific qualification in materials research.


An overview of our scientific team is available under 
Team & Topics.

The center provides the participating research groups with central experimental resources in its
Method Platforms.

The ZfM coordinates the JLU research profile area ("area of
potential") 
Material and Energy (focus: storage materials). 

Further information on our tasks can be found under
About us.

April

Picture of the Month - April 2025

Segmentation of focused ion beam-scanning electron microscopy (FIB-SEM) images using deep learning and synthetic data

All-solid-state batteries (ASSBs) are a promising candidate to obtain higher energy densities in electrochemical energy storage devices. However, the microstructure of ASSBs is a critical factor influencing not only their energy storage capacity but also essential performance parameters such as cycle life, mechanical stability, and overall reliability.

Segmentation of FIB-SEM images is important to numerically study the microstructure of cathode composites. Doing this by hand is very labour intensive and can lead to inaccurate segmentations because of human bias or other external influences.

Current state-of-the-art networks were used for the segmentation with feature encoders that were pretrained on large public datasets, to reduce the computational cost of training the model. The novelty in our approach was the use of synthetic data for training.

Only a few images needed to be manually segmented for the testing and validation of the final model. This greatly reduced the labour needed and the use of synthetic data enabled us to create a very diverse and large training set of approximately 140,000 images.

This resulted in good final scores as shown in the bar plot on the right. The intersection over union (IoU) score, which scores the overlap between the prediction and the ground truth, for the pore phase reached 44.6%, indicating moderate segmentation performance in this class. In comparison, significantly higher IoU scores were obtained for the solid phases, with 82.5% for the cathode active material and 91.5% for the solid electrolyte.

The Image of the Month illustrates the overall workflow of our approach.

This picture was submitted by Moritz Pawlowsky (group of Dr. Anja Bielefeld).

Further insights into the research activities of the ZfM groups can be found in the Gallery.

Podcast

Bericht 2020-2021